{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/7d196f914db442a592a66ec75c1f9efb\" frameborder=\"0\" width=\"1664\" height=\"1248\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1248,"width":1664,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1248,"thumbnail_width":1664,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/7d196f914db442a592a66ec75c1f9efb-286e016109901409.gif","duration":5018.9238,"title":"Building an AI-Powered Research Assistant with Semantic Chunking and Indexing","description":"In this video, I walk through the process of building an AI-powered MCP RAG server to help manage and index research papers effectively. We discuss the importance of semantic chunking and using the FAISS index for efficient information retrieval. I also highlight the need to ensure that our .env file is secure and not committed to the repository. I request viewers to follow along by testing the tools and implementing the phases as we develop the server. Overall, this session aims to equip you with the knowledge to create a robust system for handling academic research efficiently."}